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The paper presents an improved method for 1–24 hours load forecasting in the power system, integrating and combining different neural forecasting results by an ensemble system. We will integrate the results of partial predictions made by three solutions, out of which one relies on a multilayer perceptron and two others on self-organizing networks of the(More)
| In this paper we derive and analyze un-supervised adaptive on-line algorithms for instantaneous blind separation of sources (BSS) in the case when sensors signals are noisy and they are mixture of unknown number of independent source signals with unknown statistics. Nonlinear activation functions are rigorously derived assuming that source have(More)
This paper is addressed to economic problems for which many different solutions (models) can be proposed. In such situation the ensemble approach is a natural way to improve the final prediction results. In particular, we present the method for the prediction improvement in multi-agent environment based on the multivariate decompo-sitions. As a(More)